Title

Author

Date of Award

1-1-2019

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Biomedical Engineering

First Advisor

Kouhyar Tavakolian

Abstract

Drowsiness is a transitional psychophysiological state from alertness towards sleep, which decreases concentration and increases response time. Drowsiness during duty hours is common for in-flight pilots due to frequent travel across different time zones, extended duty hours as well as circadian rhythm disruption. Hence, drowsy flying is one of the leading reasons for increased risk of accidents, especially in commercial aviation. Mainly three approaches (i.e., vehicle-based, behavioral, and physiological signal based) are used for onboard drowsiness detection. Among them, physiological signal-based approach is advantageous for early detection of drowsiness with reasonable accuracy due to the strong relationship among some of the physiological signals (e.g., cardiac signal, brain wave) and psychophysiological states. Continuous monitoring of these physiological signals can be useful for early drowsiness detection. In this study of pilots’ drowsiness detection, potentials of Electroencephalogram (EEG), Electrocardiogram (ECG), and Photoplethysmogram (PPG) have been explored for on-board wearable drowsiness detection and warning system design. ECG, ear PPG, EEG, and vertical Electrooculogram (EOG) were recorded from 18 commercially rated pilots from 02:00 AM to 04:30 AM during simulated flight operation. In the case of EEG analysis, power spectral density (PSD) estimation has been used. Relative band power changes during microsleep (MS,15s) periods compared to baseline periods were tested for four EEG frequency bands (delta (δ, 0.5-4Hz), theta (θ, 4-8Hz), alpha (α, 8-13Hz), and beta (β, 13-30Hz)) from five brain regions ((Frontal, F), (Central, C), (Parietal, P), (Temporal, T), and (Occipital, O)). Delta band power reduced significantly (p

Recommended Citation

Majumder, Shubha, "Potentials Of Physiological Signals To Implement A Wearable Drowsiness Detection And Warning System For Pilots" (2019). Theses and Dissertations. 2861.
https://commons.und.edu/theses/2861